



import numpy as np







def  lowerCAmelCase_	(lowerCAmelCase__:		np.ndarray		,						lowerCAmelCase__:		float  ):





			"""simple docstring"""


			return np.where(vector > 0		,						lowerCAmelCase__		,						(alpha * (np.exp(lowerCAmelCase__  ) - 1))  )


if __name__ == "__main__":
	import doctest

	doctest.testmod()











import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


a      :  Optional[int]			     =			logging.getLogger(__name__)







@dataclass
class 				_a				(   _lowerCAmelCase					):
			A      		=	field(
			    default=0.0 ,		metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}					)
			A      		=	field(default=_lowerCAmelCase ,		metadata={'''help''': '''Whether to SortishSamler or not.'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}					)
			A      		=	field(default=_lowerCAmelCase ,		metadata={'''help''': '''whether to use adafactor'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}					)
			A      		=	field(default=_lowerCAmelCase ,		metadata={'''help''': '''Dropout probability. Goes into model.config.'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}					)
			A      		=	field(
			    default='''linear''' ,		metadata={'''help''': F'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()					)}'''} ,		)






